Abstract：
:Segmentation is often required for the analysis of dynamic positron emission tomography (PET) images. However, noise and low spatial resolution make it a difficult task and several supervised and unsupervised methods have been proposed in the literature to perform the segmentation based on semi-automatic clustering of the time activity curves of voxels. In this paper we propose a new method based on spectral clustering that does not require any prior information on the shape of clusters in the space in which they are identified. In our approach, the p-dimensional data, where p is the number of time frames, is first mapped into a high dimensional space and then clustering is performed in a low-dimensional space of the Laplacian matrix. An estimation of the bounds for the scale parameter involved in the spectral clustering is derived. The method is assessed using dynamic brain PET images simulated with GATE and results on real images are presented. We demonstrate the usefulness of the method and its superior performance over three other clustering methods from the literature. The proposed approach appears as a promising pre-processing tool before parametric map calculation or ROI-based quantification tasks.
journal_name
Phys Med Bioljournal_title
Physics in medicine and biologyauthors
Mouysset S,Zbib H,Stute S,Girault JM,Charara J,Noailles J,Chalon S,Buvat I,Tauber Cdoi
10.1088/0031-9155/58/19/6931subject
Has Abstractpub_date
2013-10-07 00:00:00pages
6931-44issue
19eissn
0031-9155issn
1361-6560journal_volume
58pub_type
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